Model predictive control systems known as “MPC” are utilized to control a variety of industrial processes. Generally speaking, model predictive controllers operate on independent and dependent variables. Independent variables are manipulated variables that can be changed or moved by an operator or controller, such as settings of valve positions or setpoints for (flows, temperatures, pressures, etc.) and feed forward or disturbance variables that have a significant impact on the process or system to be controlled yet cannot be directly manipulated. Dependent variables are controlled variables having a value that can be described or predicted totally in terms of specific independent variable changes.
A model predictive controller is programmed with step response models that show how each controlled variable responds to a change in a given independent variable. These models are used to predict the future behavior of the controlled variables based on past history of the controlled, manipulated and feed forward variables. The prediction is used to calculate appropriate control actions for the manipulated variables. The model predictions are continuously updated with measured information from the process to provide a feedback mechanism for the model predictive controller.
The models that operate the model predictive controller consist of a collection of step response models that relate the controlled variables to the manipulated and feed forward variables on the basis of a unit move of the manipulated or feed forward variables and the time after such move it takes the controlled variables to reach steady state. During operation of a controller, data is maintained that records prior values of the manipulated variables, predicted values of the controlled variables and actual values of the controlled variables. The data is updated upon every execution of the controller and is used to determine a prediction error that can be applied to the model prediction as feedback to the controller.
Utilizing the current values of the manipulated variables, an open loop response is determined, that is a response that would be obtained over a prediction horizon had no further control inputs been entered. Thereafter, a set of optimized moves of the manipulated variable are predicted to obtain a closed loop response that will bring the controlled variables to target values which in practice are set within ranges. The first of the controller moves contained within the movement plan is then transmitted to local controllers that function to control equipment within the system such as flow controllers. Such local controllers can be proportional integral differential controllers that are used, for example, to control valve actuators. The foregoing process is repeated during each execution of the controller.
Model predictive control systems have been used to control air separation plants having distillation columns. In a distillation column, a multicomponent feed to be separated or fractionated is introduced into a distillation column under conditions in which an ascending vapor phase of the mixture to be separated contacts a descending phase thereof in such a manner that the vapor phase, as it ascends, become evermore rich in the light or more volatile components of the mixture and the liquid phase, as it descends, becomes evermore rich in the heavier or less volatile components of the mixture. This contact is provided by mass transfer elements that can be structured or random packing or sieve trays.
Certain types of distillation columns are designed to produce high purity and ultra high purity products, that is products having a purity of greater than approximately 99.99% percent by volume. Such columns are particularly sensitive to the liquid/vapor ratio and can exhibit multiple steady-state temperature profiles that will rapidly change from one profile to another profile based upon the amount of vapor rising in the column and the amount of heat introduced into the column. As a result, during an upset condition caused by a change in feed composition, it can be difficult, to control the liquid to vapor ratio within the column and therefore the product purity.
Another more general problem of control is that model predictive controllers can be very difficult to tune when used in connection with certain types of systems that can include distillation columns. The difficulty arises in multivariable systems in which movement of each of two or more manipulated variables effect the value of two or more common controlled variables. For instance, in the distillation column case, a reflux flow control valve position can be represented within a model predictive control system as one manipulated variable that will have an effect on the temperature in the top section of a column as well as the bottom section of the column. Generally speaking, adding reflux tends to cool the entire column. The vapor rate within the distillation column can be controlled by a valve that controls the amount of vapor within the feed to the column. The same valve can be said to control heat addition since the vapor fraction in such a system can be controlled by controlling the amount of liquid vaporized. The position of such valve can be represented in the model predictive controller by another manipulated variable that will also have an effect on both the temperature in both the top and bottom sections of the distillation column. Under such circumstances, tuning of the controller becomes a time consuming and difficult proposition.
As will be discussed, the present invention, in one aspect, relates to a method of controlling a distillation column by model predictive control in which the controller is able to react more aggressively at certain temperature levels to prevent the product from deviating from the required product purity. In another aspect, the step response models are more effectively utilized to allow the controller to be more easily tuned.